Data is essential for ML-enabled systems. Poor data will result in inaccurate predictions, which is referred to in the ML context as “garbage in, garbage out”. Hence, ML requires high-quality input data. From the viewpoint of RE, it is clear that data constitutes a new type of requirements [1]}, [2]}. Based on the Data Quality model defined in the standard ISO/IEC 25012 [3]} and our own experience, we elaborate on the data perspective. Table REF  presents the data concerns and a brief description of each one.
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